Opinion Dynamics and Unifying Principles: A Global Unifying Frame

TL;DR

This paper introduces the Global Unifying Frame (GUF), a comprehensive probabilistic framework that unifies various opinion dynamics models, enabling systematic comparison and analysis of their behaviors, attractors, and phase transitions.

Contribution

The paper develops the GUF, a novel probabilistic approach that recasts diverse opinion models into a single framework, facilitating unified analysis and revealing model equivalences.

Findings

Most stationary states are symmetry broken.

Fifty-fifty coexistence can occur.

Some models are identical within the GUF.

Abstract

I review and extend the set of unifying principles, which allow comparing all models of opinion dynamics within one single frame. Within the Global Unifying Frame (GUF), any specific update rule chosen to study opinion dynamics for discrete individual choices is recast into a probabilistic update formula. The associated dynamics is deployed using a general probabilistic sequential process, which is iterated via the repeated reshuffling of agents between successive rounds of local updates. The related driving attractors and tipping points are obtained with non-conservative regimes featuring both threshold and threshold-less dynamics. Most stationary states are symmetry broken, but fifty-fifty coexistence may also occur. A practical procedure is exhibited for several versions of Galam and Sznajd models when restricted to the use of three agents for the local updates. Comparing these various models, some are found to be identical within the GUF. Possible discrepancies with numerical simulations are discussed together with the difference between the GUF procedure and a mean field approach.